AN EFFICIENT MULTI-CRITERIA DECISION-MAKING APPROACH BASED ON HYBRIDIZING DATA MINING TECHNIQUES AN EFFICIENT MULTI-CRITERIA DECISION-MAKING APPROACH BASED ON HYBRIDIZING DATA MINING TECHNIQUES

Multiple-criteria decision-making (MCDM) that deals with multiple criteria in decision-making environments has been explicitly applied to various decision-making fields. Nevertheless, the critical issues of uncertainty and inaccuracy generally and gradually exists in the majority of the MCDM processes because of (1) prejudice and preference of decision-makers or experts as well as (2) the insufficiency information of the input and output. Therefore, this research efficiently proposed a novel method, FVM-index method, to resolve the limitations happened when MCDM is applied. The FVM-index approach, which consists of the fuzzy set theory (FST), the variable precision rough set (VPRS), and the cluster validity index (CVI) function, not only provides optimized classification results for the datasets but also filters out the uncertainty and inaccuracy instances from surveyed datasets by VPRS theory. Because the datasets are refined by the proposed FVM-index method, the decision makers will be able to effectively obtain the suitable results of MCD

Fuzzy logic based classification of faults in mechanical differential

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential

Fast Fuzzy C-Means Algorithm Incorporating Convex Combination of Bilateral Filter with Contrast Limited Adaptive Histogram Equalization

Fast Generalized Fuzzy c-means clustering algorithm (FGFCM) and its variants are effective methods for image clustering. Even though the incorporation of local spatial information to the objective function reduces their sensitivity to noise to some extent, they are still lack behind in suppressing the effect of noise and outliers on the edges and tiny areas of input image. This article proposes an algorithm to mitigate the disadvantage of FGFCM and its variants and enhances the performance of clustering

k-Partite Graph Reinforcement and its Application in Multimedia Information Retrieval

10.1016/j.ins.2012.01.003Information Sciences194224-239ISIJ

Assessing developmental footprint within an agricultural system using multi-temporal remotely sensed data.

M. Sc. University of KwaZulu-Natal, Pietermaritzburg 2014.The advent of the new political dispensation in South Africa has seen an exponential growth in the rate of land transformation and encroachment by other land uses into agricultural land in the uMngeni Local Municipality. Accurate evaluation of the rate of transformation is necessary for effective monitoring and management of the natural agricultural resources. In this regard, the use of multi-temporal remote sensing data provides efficient and cost-effective method. The current research assesses the extent to which the development footprint in uMngeni Local Municipality has affected agricultural land categories or zones, using multi-temporal remote sensing data. The study endeavoured to map and quantify the magnitude of change in built-up land cover and other infrastructure by focusing on two time intervals: the periods from 1993 – 2003 and 2003 – 2013. Medium spatial resolution Landsat image data acquired for these periods were analysed to classify and extract the built-up features to appraise the level of change. Results revealed positive change in built-up infrastructure: ~13% increase between 1993 and 2003, ~38% increase from 2003 – 2013, with overall ~32% for the 20 years (1993 – 2013) period under consideration. Next, factors possibly contributing to the encroachment of other land uses into the agricultural landscape and the potential threats to the sustainability of the agricultural system are highlighted